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Train a Lasso model given an RDD of (label, features) pairs. We run a fixed number
of iterations of gradient descent using a step size of 1.0. We use the entire data set to
compute the true gradient in each iteration.

input

RDD of (label, array of features) pairs. Each pair describes a row of the data
matrix A as well as the corresponding right hand side label y

Train a Lasso model given an RDD of (label, features) pairs. We run a fixed number
of iterations of gradient descent using the specified step size. We use the entire data set to
update the true gradient in each iteration.

input

RDD of (label, array of features) pairs. Each pair describes a row of the data
matrix A as well as the corresponding right hand side label y

Train a Lasso model given an RDD of (label, features) pairs. We run a fixed number
of iterations of gradient descent using the specified step size. Each iteration uses
miniBatchFraction fraction of the data to calculate a stochastic gradient.

input

RDD of (label, array of features) pairs. Each pair describes a row of the data
matrix A as well as the corresponding right hand side label y

Train a Lasso model given an RDD of (label, features) pairs. We run a fixed number
of iterations of gradient descent using the specified step size. Each iteration uses
miniBatchFraction fraction of the data to calculate a stochastic gradient. The weights used
in gradient descent are initialized using the initial weights provided.

input

RDD of (label, array of features) pairs. Each pair describes a row of the data
matrix A as well as the corresponding right hand side label y

numIterations

Number of iterations of gradient descent to run.

stepSize

Step size scaling to be used for the iterations of gradient descent.

regParam

Regularization parameter.

miniBatchFraction

Fraction of data to be used per iteration.

initialWeights

Initial set of weights to be used. Array should be equal in size to
the number of features in the data.